Increased deployment of renewable energy can contribute towards mitigating climate change and improving air quality, wealth and development. However, renewable energy technologies are not free of environmental impacts; thus, it is important to identify opportunities and potential threats from the expansion of renewable energy deployment. Currently, there is no cross-national comprehensive analysis linking renewable energy potential simultaneously to socio-economic and political factors and biodiversity priority locations. Here, we quantify the relationship between the fraction of land-based renewable energy (including solar photovoltaic, wind and bioenergy) potential available outside the top biodiversity areas (i.e. outside the highest ranked 30% priority areas for biodiversity conservation) within each country, with selected socio-economic and geopolitical factors as well as biodiversity assets. We do so for two scenarios that identify priority areas for biodiversity conservation alternatively in a globally coordinated manner vs. separately for individual countries. We show that very different opportunities and challenges emerge if the priority areas for biodiversity protection are identified globally or designated nationally. In the former scenario, potential for solar, wind and bioenergy outside the top biodiversity areas is highest in developing countries, in sparsely populated countries and in countries of low biodiversity potential but with high air pollution mortality. Conversely, when priority areas for biodiversity protection are designated nationally, renewable energy potential outside the top biodiversity areas is highest in countries with good governance but also in countries with high biodiversity potential and population density. Overall, these results identify both clear opportunities but also risks that should be considered carefully when making decisions about renewable energy policies.

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The United Kingdom currently reports nitrous oxide emissions from agriculture using the IPCC default Tier 1 methodology. However Tier 1 estimates have a large degree of uncertainty as they do not account for spatial variations in emissions. Therefore biogeochemical models such as DailyDayCent (DDC) are increasingly being used to provide a spatially disaggregated assessment of annual emissions. Prior to use, an assessment of the ability of the model to predict annual emissions should be undertaken, coupled with an analysis of how model inputs influence model outputs, and whether the modelled estimates are more robust that those derived from the Tier 1 methodology. The aims of the study were (a) to evaluate if the DailyDayCent model can accurately estimate annual N 2 O emissions across nine different experimental sites, (b) to examine its sensitivity to different soil and climate inputs across a number of experimental sites and (c) to examine the influence of uncertainty in the measured inputs on modelled N 2 O emissions. DailyDayCent performed well across the range of cropland and grassland sites, particularly for fertilized fields indicating that it is robust for UK conditions. The sensitivity of the model varied across the sites and also between fertilizer/manure treatments. Overall our results showed that there was a stronger correlation between the sensitivity of N 2 O emissions to changes in soil pH and clay content than the remaining input parameters used in this study. The lower the initial site values for soil pH and clay content, the more sensitive DDC was to changes from their initial value. When we compared modelled estimates with Tier 1 estimates for each site, we found that DailyDayCent provided a more accurate representation of the rate of annual emissions.

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